Exploitation of Semantic Keywords for Malicious Event Classification

General image classification approaches differentiate classes using strong distinguishing features but some classes cannot be easily separated because they contain very similar visual features. To deal with this problem, we can use keywords relevant to a particular class. To implement this concept we have newly constructed a malicious crowd dataset which contains crowd images with two events, benign and malicious, which look similar yet involve opposite semantic events. We also created a set of five malicious event-relevant keywords such as police and fire. In the evaluation, integrating malicious event classification with recognition output of these keywords enhances the overall performance on the malicious crowd dataset.